AIRCC's International Journal of Computer Science and Information Technology

Open Access Open Access  Restricted Access Subscription Access

New Behavioral Segmentation Methods to Understand Consumers in Retail Industry


Affiliations
1 Universiti Sains, Malaysia
2 Mansoura University, Egypt
 

Behavioral segmentation is considered as one of the most important concepts of modern marketing. Traditional customer segmentation models require months of analytical work, resulting in discrete consumers’ insights that are outdated to match the dynamic body of the consumers they are meant to represent. Personalization and consumer experience are make or break factors for the retail industry. This study looks towards maximizing Consumer Lifetime Value (LTV) to accommodates the dynamics in consumer shopping behavior for a medium size retailer. using (LTV) matricto investigate behavioral changes in the consumer shopping history gaining knowledge from behavioral and demographic variables stored in POS database converted into RFM dataset format. In addition, this study applies soft clustering Fuzzy C-Means (FCM) and hard clustering Expectation Maximization (EM) algorithms to classify individual consumers exhibit similar purchase history into specific groups. For measuring the algorithms accuracy, weuse cluster quality assessment (CQA). The CQA shows EM algorithm scales much better than Fuzzyy C-Means algorithm with its ability to assign good initial points in the smaller dataset.

Keywords

Customer Segmentation, Clustering, LTV Matric, Retailing.
User
Notifications
Font Size

  • APS Meeting Abstracts (Vol. 1, p. 11002).

  • Azevedo, A. (Ed.). (2014). Integration of Data Mining in Business Intelligence Systems. IGI Global.

  • Bernard, H. R. (2011). Study methods in anthropology: Qualitative and quantitative approaches. Rowman Altamira.

  • Birant, D. (2011). Data Mining Using RFM Analysis, INTECH Open Access Publisher.

  • Broderick, A., & Pickton, D. (2005). Integrated marketing communications. Pearson Education UK.

  • concept to implementation. Prentice-Hall, Inc.

  • Carter, N. M., Stearns, T. M., Reynolds, P. D., & Miller, B. A. (1994). New venture strategies: Theory development with an empirical base. Strategic Management Journal, 15(1), 21-41.

  • Chen, D., Sain, S. L., & Guo, K. (2012). Data mining for the online retail industry: A case study of RFM model-based consumer segmentation using data mining. Journal of Database Marketing & Consumer Strategy Management, 19(3), 197-208.

  • Chen, H., Chiang, R. H., & Storey, V. C. (2012). Business intelligence and analytics: from big data to a big impact. MIS Quarterly, 1165-1188.

  • Cleveland, M., Papadopoulos, N., & Laroche, M. (2011). Identity, demographics, and consumer behaviors: International market segmentation across product categories. International Marketing Review, 28(3), 244-266.

  • Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the royal statistical society. Series B (methodological), 1-38.

  • Dipanjan, D., Satish, G., & Goutam, C. (2011). Comparison of Probabilistic-D and k-Means Clustering in Segment Profiles for B2B Markets. SAS Global Forum.

  • Drăghici, S. (2003). Data analysis tools for DNA microarrays. CRC Press.

  • Dwyer, F. R. (1997). Consumer lifetime valuation to support marketing decision making. Journal of interactive marketing, 11(4), 6-13.

  • Elby, A. (2015, April). The new AP Physics exams: Integrating qualitative and quantitative reasoning. In APS Meeting Abstracts (Vol. 1, p. 11002).

  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). From data mining to knowledge discovery in databases. AI magazine, 17(3), 37.

  • Fayyad, U., Piatetsky-Shapiro, G., & Smyth, P. (1996). The KDD process for extracting useful knowledge from volumes of data. Communications of the ACM, 39(11), 27-34.

  • Fraley, C., & Raftery, A. E. (2006). MCLUST version 3: an R package for normal mixture modeling and model-based clustering. WASHINGTON UNIV SEATTLE DEPT OF STATISTICS.

  • Gal, A. (2010). Competitiveness of small and medium sized enterprises-a possible analytical framework. HEJ: ECO-100115-A.

  • Goyat, S. (2011). The basis of market segmentation: a critical review of literature. European Journal of Business and Management, 3(9), 45-54.

  • Gunaseelan, D., & Uma, P. (2012). An improved frequent pattern algorithm for mining association rules. International Journal of Information and Communication Technology Study, 2(5).

  • Hossein Javaheri, S., (2008), Response Modeling in Direct Marketing: a data mining based approach for target selection, Masters thesis, epubl.luth.se/1653-0187/2008/014/LTU-PB-EX-08014-SE.pdf.

  • Kashwan, K. R. & C. Velu (2013). Consumer Segmentation Using Clustering and Data Mining Techniques. International Journal of Computer Theory & Engineering 5(6): 856-861.

  • Khajvand, M., & Tarokh, M. J. (2011). Estimating consumer future value of different consumer segments based on adapted RFM model in retail banking context. Procedia Computer Science, 3, 1327-1332.

  • Khajvand, M., Zolfaghar, K., Ashoori, S., & Alizadeh, S. (2011). Estimating consumer lifetime value based on RFM analysis of consumer purchase behavior: Case study. Procedia Computer Science, 3, 57-63.

  • Kolyshkina, I., Nankani, E., Simoff, S., & Denize, S. (2010). Retail analytics in the context of “Segmentation, Targeting, Optimization” of the operations of convenience store franchises. Anzmac.

  • Lefait, G., & Kechadi, T. (2010, February). Consumer segmentation architecture based on clustering techniques. In Digital Society, 2010. ICDS'10. Fourth International Conference on (pp. 243-248). IEEE.

  • Marcus, C. (1998). A practical yet meaningful approach to consumer segmentation. Journal of consumer marketing, 15(5), 494-504.

  • McCarty JA, Hastak M (2007). Segmentation approaches in data-mining: A comparison of RFM, CHAID, and logistic regression. J. Bus. Res., 60: 656-662.

  • Ramageri, B. M., & Desai, B. L. (2013). Role of data mining in retail sector. International Journal on Computer Science and Engineering, 5(1), 47.

  • Safari, M. (2015). Consumer Lifetime Value to managing marketing strategies in the financial services. International Letters of Social and Humanistic Sciences, 1(2), 164-173.

  • Tsiptsis, K. K., & Chorianopoulos, A. (2011). Data mining techniques in CRM: inside consumer segmentation. John Wiley & Sons.

  • Tufféry, S. (2011). Data mining and statistics for decision making, John Wiley & Sons.

  • Ziafat, H., & Shakeri, M. (2014). Using Data Mining Techniques in Consumer Segmentation. International Journal of engineering Study and Applications, 1(4), 70-79.

  • Nascimento, S., Mirkin, B., & Moura-Pires, F. (2000). A fuzzy clustering model of data and fuzzy cmeans. In Fuzzy Systems, 2000. FUZZ IEEE 2000. The Ninth IEEE International Conference on (Vol. 1, pp. 302-307). IEEE.

  • D'Urso, P., Prayag, G., Disegna, M., & Massari, R. (2013). Market segmentation using bagged fuzzy c–means (BFCM): Destination image of Western Europe among Chinese travelers.

  • K.M. Bataineh, M.Naji, M.Saqer “A Comparison Study between Various Fuzzy Clustering Algorithms” vol.5 no.4 Aug. 2011

  • Schafer, J. L. (1997). Analysis of incomplete multivariate data. Chapman and Hall/CRC.

  • Altman, D. G., & Bland, J. M. (1995). Statistics notes: the normal distribution. Bmj, 310(6975), 298.

  • Hillary, R. (Ed.). (2017). Small and medium-sized enterprises and the environment: business imperatives. Routledge.

  • Storey, D. J. (2016). Understanding the small business sector. Routledge.

  • An, J., Kwak, H., Jung, S. G., Salminen, J., & Jansen, B. J. (2018). Customer segmentation using online platforms: isolating behavioral and demographic segments for persona creation via aggregated user data. Social Network Analysis and Mining, 8(1), 54.

  • Weinstein, A. T. (1994). Market Segmentation: Using Demographics, Psychographics and Other Niche Marketing Techniques to Predict Customer Behavior. Probus Publishing Co.

  • Brito, P. Q., Soares, C., Almeida, S., Monte, A., & Byvoet, M. (2015). Customer segmentation in a large database of an online customized fashion business. Robotics and Computer-Integrated Manufacturing, 36, 93-100.


Abstract Views: 630

PDF Views: 482




  • New Behavioral Segmentation Methods to Understand Consumers in Retail Industry

Abstract Views: 630  |  PDF Views: 482

Authors

Fahed Yoseph
Universiti Sains, Malaysia
Nurul Hashimah Ahamed Hassain Malim
Universiti Sains, Malaysia
Mohammad AlMalaily
Mansoura University, Egypt

Abstract


Behavioral segmentation is considered as one of the most important concepts of modern marketing. Traditional customer segmentation models require months of analytical work, resulting in discrete consumers’ insights that are outdated to match the dynamic body of the consumers they are meant to represent. Personalization and consumer experience are make or break factors for the retail industry. This study looks towards maximizing Consumer Lifetime Value (LTV) to accommodates the dynamics in consumer shopping behavior for a medium size retailer. using (LTV) matricto investigate behavioral changes in the consumer shopping history gaining knowledge from behavioral and demographic variables stored in POS database converted into RFM dataset format. In addition, this study applies soft clustering Fuzzy C-Means (FCM) and hard clustering Expectation Maximization (EM) algorithms to classify individual consumers exhibit similar purchase history into specific groups. For measuring the algorithms accuracy, weuse cluster quality assessment (CQA). The CQA shows EM algorithm scales much better than Fuzzyy C-Means algorithm with its ability to assign good initial points in the smaller dataset.

Keywords


Customer Segmentation, Clustering, LTV Matric, Retailing.

References